Understanding declining fluidity in the U.S. labor market.

Molloy, Raven ; Smith, Christopher L. ; Trezzi, Riccardo 等

IV.C. Implications of Declining Fluidity

Regardless of the cause, less fluidity in the labor market leads to fewer opportunities for workers to renegotiate their current employment arrangements using outside options as leverage. (38) In a key paper, Paul Beaudry and John DiNardo (1991) argue that we can observe the results of such renegotiations by testing for the impact of labor market conditions on wages over the course of a worker's employment with a firm. To paraphrase their central claim: If broader market conditions at a given point in time affect a worker's wages, then the worker must have had an outside option that he or she could credibly threaten to accept at that time. Thus, they argue that in a spot market for labor, wages should be related to contemporaneous labor market conditions. Conversely, if wages are determined by long-term implicit contracts between workers and firms, then contemporaneous conditions should have little effect. Rather, the relationship between wages and labor market conditions should depend on workers' opportunities or ability to move across firms. If workers have limited mobility across firms, then wages are set at the start of a new worker-firm relationship, and wages should reflect labor market conditions at the time the worker was hired. By contrast, if workers have perfect mobility across firms, then the contract is reset whenever workers receive a better outside option, in which case wages should be related to the best labor market conditions since the worker was hired.

[FIGURE 12 OMITTED]

Using data from the PS1D and CPS in the late 1970s and early 1980s, Beaudry and DiNardo (1991) find the strongest support for the implicit contract model with perfect worker mobility. Darren Grant (2003) finds similar results using the original cohorts of the National Longitudinal Surveys and the NLSY79. We build on these studies by examining how these relationships have changed during the past three decades. In particular, we estimate a log wage equation that includes labor market conditions at three points in time: contemporaneous conditions, conditions at the time a worker started his or her current job, and the most favorable conditions that obtained from the time the job started to the present. We use the annual national unemployment rate for all individuals ages 16 and older as our measure of labor market conditions. (39) Other controls include age, age squared, employer tenure, and employer tenure squared. We estimate the model in the PSID, the NLSY, and the CPS (surveys that include the tenure supplement). The PSID and NLSY specifications also include individual fixed effects, whereas the CPS specification includes educational attainment, indicators for nonwhite and ever married, and industry and region fixed effects. The one notable difference between our specification and that in Beaudry and DiNardo (1991) is that our samples are long enough to include a quadratic time trend, so that our results are not driven by trends in unemployment and wages.

We find evidence that the role of external labor market conditions in wage setting has changed, at least since the 1990s. As shown in table 7, as in the earlier two papers, we find that the minimum unemployment rate after a worker was hired had a large impact on wages in the 1980s and into the 1990s. However, in the 2000s the connection between wages and the minimum unemployment rate is much weaker. (40) At the same time, initial conditions seem to have become more important for wages, although this correlation is not significant in our smaller data samples (NLSY97, or young workers in the PSID). Thus, it seems that in the 2000s, workers and employers renegotiated wage contracts less frequently with improving market conditions than they did in the 1980s and 1990s, a result that is consistent with the secular decline in labor market fluidity. (41) The question of when and by how much worker compensation adjustments happen is a key area for future research that we take up below.

Declining fluidity may have other effects as well. For example, Davis and Haltiwanger (2014) find that declining worker and job reallocation has reduced the employment rates of some groups, perhaps because labor market fluidity and job reallocation are associated with career advancement and productivity growth. Another possibility is that declining fluidity could make workers reluctant to separate voluntarily, leading to "precautionary" job holding, as the likelihood of finding another job within a given time frame is reduced. In this case, the fraction of separations that are voluntary should fall, and, correspondingly, the fraction that are involuntary should rise. To investigate this possibility, we use the same state panel trend regression approach from previous sections and regress the cycle-adjusted state trend in the share of the unemployed who report that their separations were involuntary ("involuntary unemployment") on the state trend in fluidity. (42) We find that states with larger declines in fluidity saw higher shares of their unemployed who reported an involuntary separation was behind their unemployment. The relationship is substantial: A decrease in fluidity of 1 standard deviation is associated with an increase of 0.33 standard deviation in involuntary shares of unemployment. This result suggests that the effect of fluidity on precautionary job holding should be explored further--although, alternatively, this result may be due to a compositional effect if voluntary separations have fallen more than involuntary separations for other reasons.

V. Concluding Discussion: What Have We Learned, and What Should Future Research Tackle Next?

Is the U.S. labor market becoming less fluid? An accumulation of evidence on declines in assorted worker transition rates, as well as declining turnover within firms, has led economists to ask if these separate findings represent a more general shift toward fewer transitions within the U.S. labor market. Motivated by this question, we first seek to demonstrate a statistical connection between various measures of labor market transitions. To this end, we construct a unique measure of the trend in labor market fluidity by combining trends on the major flows into and out of employment with job-to-job transitions, interstate migration, and job creation and destruction. Our analysis suggests that labor market fluidity has declined 10 to 15 percent during the past three to four decades, indicating that this trend has been sizable.

One advantage of our measure of labor market fluidity is that it extends over a long period, from the late 1960s to the present in its longest version, which allows us to investigate when the decline in fluidity began. The data suggest that the declines began at least in the early 1980s, and perhaps in the 1970s. The result--that this trend has persisted for at least three decades--suggests that the causes of this trend also must have persisted for a long period.

We devote the remainder of the paper to trying to understand the cause or causes of the decline in labor market fluidity. Although we are ultimately unable to identify a clear reason for the decline, we make progress along several key dimensions. We first verify that demographic changes can only explain a limited portion of the general decline. Changes in labor force participation and educational attainment are relevant for some types of transitions and some demographic groups, but overall, the general patterns are similar for most types of workers that we examine.

Next, using state-level variation in trends in labor market fluidity, we find that fluidity is unrelated to most worker characteristics in the state as well as to the industrial composition of the state. One interesting exception is that states in the Mountain and Pacific census divisions have experienced larger declines in fluidity, even conditional on a wide variety of state characteristics. We also find that declines have been smaller in states with larger initial shares of middle-skill jobs. It seems possible that the displacement of routine-intensive jobs may have increased labor market transitions for these workers, dampening the general decline in fluidity.

Finally, we consider a number of concrete explanations for declining labor market fluidity, grouped into explanations with benign implications for the aggregate economy and explanations with less benign implications. The benign explanations that we consider are improved matches between workers and firms; enhanced flexibility in compensation that ties compensation more directly to productivity; and more intensive employer-provided training. The less benign explanations that we consider are sclerosis, as a shrinking fraction of young workers reduces the liquidity of the labor market for workers of all ages; declines in social capital that make hiring and job searches more difficult; and an increase in regulatory barriers to labor market transitions. Although our approach to assessing these explanations is descriptive and, in some cases, relies on the previous literature, we conclude that most of these potential channels are unlikely to explain the decline in fluidity. One exception is that states with a larger decrease in the fraction of people who report that strangers can be trusted tend to have experienced larger declines in labor market fluidity, suggesting that explanations related to social capital and networks are worth exploring in future research. We also believe the question of whether compensation adjustment within and across jobs has changed deserves more attention.

Although the evidence on potential explanations in this paper is far from definitive, in general we find little role for explanations that are related solely to worker characteristics or to general labor market institutions. Consequently, research into the connection between firm characteristics and declining labor market fluidity seems like a promising avenue for future research. We can rule out the simple effect of industrial composition, and other studies have found a limited role for industrial composition, including Hyatt and Spletzer (2013) and Decker and others (2014a). However, there are many other firm characteristics that we are unable to explore with our data. For example, Decker and others (2014a) and Davis and Haitiwanger (2014) show that the secular decline in job creation and destruction is at least partly related to a decline in the number of smaller and younger firms. Research on the role that firm size and age may play in declining fluidity measures has so far focused on compositional effects, which may not account for all the ways in which these characteristics affect job turnover. The decline in new firm formation dates from the 1970s (Pugsley and Sahin 2015), which aligns well with the timing of the downward trend in fluidity. Therefore, a more detailed examination of changes in how firms and workers interact--particularly across firm size and age groups--would be quite valuable.

Getting inside the black box of the employment relationship also seems likely to be helpful. A series of recent papers document the important role of firms in rising earnings inequality (Card, Heining, and Kline 2013; Barth and others 2014; Song and others 2015). Namely, a substantial portion of the widening in earnings inequality during the last several decades has been due to a growing dispersion of earnings across firms, rather than increases in dispersion within firms. This trend could contribute to declining labor market fluidity if rising disparity in pay across firms extends workers' job search time. However, the evidence assembled so far suggests that the rise in earnings inequality is unlikely to be behind the decline in fluidity. First, the secular rise in earnings inequality has been linked to the decline in demand for middle-skilled workers, and if anything it seems as if the changes in demand for skills have dampened the long-run decline in labor market fluidity.

To further examine this relationship, we requested special tabulations of establishment-level total compensation inequality from the Office of Compensation and Working Conditions at the Bureau of Labor Statistics. (43) As summarized in table 8, the rise in the 90/10 differential in average compensation across establishments (a proxy for firms) is largely a coastal story, with noncoastal census divisions having experienced little increase in establishment-level pay inequality. This pattern does not align well with the geographic pattern that we observe in labor market fluidity. Thus, an increase in firm heterogeneity seems unlikely to explain declining fluidity, although it is possible that more research could be done here. But a more promising avenue would be an exploration of the wage and compensation changes that workers experience both within and across firms. Simply robustly documenting these changes over time would be a helpful step, because there has been very little research in this area.

Such an analysis would allow a cleaner assessment of whether within-firm earnings volatility has increased, possibly signaling the stronger connection between compensation and productivity discussed above. It would also allow for an examination of whether the return to changing employers has fallen, which might have occurred if large firms have offered a less variable set of contracts to a given worker. (44) Enhanced information about firms would also allow an exploration of how firm output volatility relates to hiring and separation, and whether these relationships have changed over time.

Most publicly available data sets are unsuitable for examining wage or compensation changes within firms. The use of matched employer-employee data, which tend to be large and of high quality, would be an appropriate resource to explore. Although it is also often the case that demographic information is more limited in these data sets, our analysis suggests that demographics can largely be set aside. Conversely, many such data sets are only available for recent decades, so one would need to extrapolate from this evidence to the entire three to four decades during which labor market fluidity has been declining.

Another potentially fruitful direction for future research would be to explore secular changes in terms of employment, such as information on screening and hiring practices, as well as firm-provided training. And enhanced matching across administrative data sets might help us understand more about how firms use firing versus other types of separations.

Finally, another important topic for future research is to pursue a clearer understanding of the effects of the secular decline in labor market fluidity. We have shown that this trend appears to have coincided with a reduced frequency of wage renegotiations between employers and workers, which might signal additional rigidities in the compensation-setting process, or a diminished need to renegotiate. In addition, states with larger declines in labor market fluidity have experienced an increase in the fraction of involuntary separations. Davis and Haltiwanger (2014) show that decreases in worker and job reallocation are associated with lower employment rates, especially of the young and less educated. Taken all together, this evidence strongly suggests that the first-order effect of the aggregate decline in fluidity is unlikely to be benign, at least not for workers.

Although this paper has raised at least as many questions as it has answered, we hope that it has made a few things clear: Labor market fluidity has been declining since at least the 1980s, and it has been fairly broad-based across types of workers and broad industrial sectors. Nevertheless, there are marked geographic differences in the extent of declines in fluidity that are not easily explained by the standard demographic or socioeconomic characteristics of the people who live there. Because this trend has persisted for so long and touches on so many types of workers and firms, more research on the causes and consequences of this trend would be extremely valuable.

ACKNOWLEDGMENTS We are grateful to Stephanie Aaronson, Josh Gallin, and Andrea Stella for helpful comments; to our discussants, Erica Groshen and John Haltiwanger; and to our editor, James Stock. For providing research data, we thank Frank Limehouse of the Chicago Census Research Data Center, Jaesok Son of the National Opinion Research Center, and Brooks Pierce and Jesus Ranon of the Office of Compensation and Working Conditions at the Bureau of Labor Statistics. For helpful research assistance, we thank Ning Jia. All errors and omissions remain ours. The views expressed in this paper are those of the authors and do not necessarily reflect those of the Board of Governors or the Federal Reserve System.

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Comments and Discussion

COMMENT BY

ERICA L. GROSHEN This ambitious paper by Raven Molloy, Christopher Smith, Riccardo Trezzi, and Abigail Wozniak explores an important topic: the apparent decline in labor market fluidity over time. The authors look at a variety of flows--those between the various labor market states, job creation and destruction, job-to-job transitions, and interstate migration. By establishing a common trend among a variety of worker and job flows, they test a number of hypotheses to try to find one explanation for the decline in fluidity.

Their findings are intriguing. Although, in the authors' own words, "this paper has raised at least as many questions as it has answered," it makes a valuable contribution to the literature. And it is a fine demonstration of the value of having multiple, high-quality data series that can each provide a unique perspective on an important phenomenon.

My comments are organized into six topics, each with some points about the paper and some suggestions for future research.

To begin with, it would be helpful to have a clear theoretical motivation for why we should expect that the same factors that explain the decline in fluidity among worker flows would also explain the decline in the fluidity of job flows. It seems that there should be different hypotheses for these two types of flows. And if this is true, how and when should the decline in worker flows lead to a decline in job flows, and vice versa? One might also expect different causal factors behind the decline in fluidity among the various worker flows. Thus, can the theories be differentiated more clearly as to whether worker or job flows are directly affected, and which type of flow in particular--NE, EN, EU, UE, JtJ, quits, layoffs, births, deaths, by demographics, industry, state, and so on?

Second, the authors readily acknowledge that though not explaining the entire decline in fluidity, demographics do appear to play a substantial role.

Further exploration of the role of demographics would seem to be a topic for future investigation.

To this end, it might be useful to break down the worker flows into specific demographic groups, and to do the same time series detrending technique already used in the paper. This would give additional flows to test the robustness of the common component that the authors find in the eight flows they currently use. If the common component disappears when breaking down worker flows by demographics, that would actually be strong evidence that demographics explain a major portion of the decline in fluidity. This analysis would help in explaining the importance of demographics and seeing how much of the decline in fluidity remains to be explained after we consider certain key demographic changes over this period (baby boomers retiring, women's stronger attachment to the labor force, and so on).

Third, most of the hypotheses tested in the paper seem to focus on trying to explain worker flows, with little attention to explaining job flows. There is little discussion about how changes in firm structure might be an important explanation for the job flows.

For instance, there may be reason to think that increasing vertical disintegration over time (that is, more complex production networks) would lead to a decline in volatility in purchasing industries, which could explain some of the declining fluidity in job flows. Consider a temporary help agency that supplies workers to companies in a town in order to meet uncorrelated transient demand shifts. Compared with the case where the customers each hired and fired workers as needed, using a temporary help agency would decrease the fluidity of the labor market. Though Vasco Carvalho (2014) emphasizes that big sectoral hubs can lead to aggregate fluctuations, he notes that other network patterns can dampen idiosyncratic movements.

A potentially related question is how this decline in fluidity relates to the previous literature on the "Great Moderation" begun by Margaret McConnell and Gabriel Perez-Quiros (2000). This line of thought and subsequent work on the impact of just-in-time inventory management suggests that job flows warrant further exploration. Job creation and destruction could be broken down by industry to add even more flows to the time series detrending exerce at the beginning of the paper. It would be interesting to see how much of the common component remains when considering industries separately, and perhaps it would illuminate some new explanations for the decline.

One possibility for future work is to use input-output tables to test hypotheses about changes in industries and vertical integration over time. An examination of input and output patterns with reference to network theory predictions would seem potentially fruitful for pursuing an explanation.

Fourth, as the authors note, removing the effects of the business cycle is not easy. Future research will no doubt delve further into the special importance of the choice of bandwidths in the time series analysis, which determines the degree of smoothing.

In addition, I cannot help but wonder if it would be helpful to distinguish between trends before and after the Great Recession. In particular, the fall in flows from not in the labor force to employment seems to have primarily occurred during the Great Recession. Given the possible concern about a finite sample, repeating the analysis both with and without the Great Recession would be a good robustness check.

Fifth, I raise a puzzle posed by the National Longitudinal Surveys of Youth (NLSY) data. In NLSY97, individuals on average held more jobs from age 18 through 26 than had been observed over the same age range in NLSY79. This may be worth exploring further; on the surface at least, this observation would seem to be evidence against the declining fluidity story.

Sixth and finally, I offer two more institutional changes that likely contributed to declining fluidity--for future study. The institution of experience rating for unemployment insurance in the 1980s was intended to reduce the overuse of temporary layoffs by employers. To the extent that it succeeded, we would see a decline in fluidity. In addition, tighter bankruptcy laws would reduce fluidity over time, as they reduced the number of business deaths and, therefore, births.

All told, I agree with the authors on the value of reaching across data series to see if a common trend exists, organizing the explanations to bring them to the data, and considering implications. This paper advances the discussion in all three ways. The multitude of remaining questions raised by this work are hardly a sign of failure. 1 look forward to further research on this topic from this team and others.

REFERENCES FOR THE CROSHEN COMMENT

Carvalho, Vasco M. 2014. "From Micro to Macro via Production Networks." Journal of Economic Perspectives 28, no. 4: 23-48.

McConnell, Margaret M., and Gabriel Perez-Quiros. 2000. "Output Fluctuations in the United States: What Has Changed since the Early 1980's?" American Economic Review 90, no. 5: 1464-76.

COMMENT BY

JOHN HALTIWANGER Raven Molloy, Christopher Smith, Riccardo Trezzi, and Abigail Wozniak have written an interesting and informative paper about the changing patterns of labor market fluidity in the U.S. economy. Consistent with the burgeoning recent literature on this topic, this paper confirms a decline in indicators of labor market fluidity during the last several decades. The contributions of this paper to this recent literature are multifold. First, the authors use the Current Population Survey's (CPS) gross flows data to quantify some of the important components of the indicators of labor market fluidity going back to the late 1960s. Second, they focus much of their attention on possible factors underlying the changes in labor market fluidity and possible implications. They conduct a number of interesting exercises to explore possible sources of the decline.

I am very sympathetic to the broad themes of the paper, and also value the contribution of using the CPS gross flows to shed additional light on the issues that have arisen regarding indicators of declining labor market fluidity. For economy-wide measures of worker reallocation (defined as hires plus separations), recent research has been restricted to indicators starting in 1990 (Davis and Haltiwanger 2014). For job reallocation (defined as job creation plus job destruction), measures at the economy-wide level are available starting in the late 1970s and back to 1947 for the manufacturing sector (Davis, Faberman, and Haltiwanger 2006). As discussed below in more detail, job reallocation only captures a portion of the overall pace of worker reallocation (defined as hires plus separations). A welcome addition to the literature in this paper is the authors' use of the CPS gross flows as a potential source of economy-wide measures of worker reallocation starting in 1967. However, the CPS gross flows are missing job-to-job flows--a key aspect of worker reallocation until the 1990s. But one interesting finding from the CPS gross flows is that at least some components of worker reallocation have been declining since the late 1960s. Taken at face value, this is an important finding for the literature because it suggests that the underlying factors accounting for the declining indicators of labor market fluidity may date back to the late 1960s.

It turns out, however, that comparisons of the CPS gross flows--based measures do not exhibit the same patterns of declining fluidity in the post-1990 period from the survey and administrative sources that are available starting in that period. This raises questions about how to interpret the main findings of this paper, as well as the paper's exercises focusing on the possible sources and implications of declining fluidity that depend on using the spatial variation in declining trends in the CPS gross flows.

To understand the comparisons with other sources, it is helpful to take a step back to consider concepts and definitions. A useful starting point is the identities in the authors' equations 1 and 2, which relate total hires to the flows of workers from nonemployment (unemployment plus those not in the labor force) plus job-to-job flows, and total separations to the flows of workers to nonemployment plus job-to-job flows. The CPS gross flows permit measuring the flows to and from nonemployment (the NE, UE, EN, and EU terms in the authors' equations 1 and 2) from the late 1960s to the present. Direct measures of job-to-job flows from the CPS only become available starting in 1994, with the CPS redesign (Fallick and Fleischman 2004). These measures are available on a monthly basis but, as is common in the literature, Molloy, Smith, Trezzi, and Wozniak focus on cumulative monthly values at a quarterly frequency, given that the CPS-based flows are quite noisy.

These identities are useful for relating the CPS gross flows-based measures of hires and separations to alternative sources. Starting in the 1990s, the Quarterly Workforce Indicators (QWI), published by the Census Bureau, yield quarterly measures of total hires and separations for the U.S. private sector. In addition, the Longitudinal Employer-Household Dynamics (LEHD) program at the Census Bureau has recently released a decomposition of the hires and separations into those that involve job-to-job flows and those that reflect hires from nonemployment and separations to non-employment (Haltiwanger, Hyatt, and McEntarfer 2015). The QWI and the LEHD job-to-job flows data are based on comprehensive, longitudinal, matched employer-employee data from administrative sources.

In addition, since 2001 the monthly, establishment-level Job Openings and Labor Turnover Survey (JOLTS) has provided monthly data on hires and separations (along with a breakdown of the latter into quits and layoffs). Steven Davis, Jason Farberman, and I (2012) have developed methods using the integration of the JOLTS micro data with the Business Employment Dynamics (BED) micro data at the Bureau of Labor Statistics (BLS) to construct backcasted measures of hires, separations, quits, and layoffs that date back to the second quarter of 1990. I refer to these as the JOLTS+BED estimates in what follows. The integrated JOLTS+BED data cover the U.S. private, nonfarm sector.

The BED is one of two administrative data-based sources of job creation and job destruction measures. The BED provides quarterly measures from the early 1990s to the present and is based on comprehensive administrative data covering more than 6 million establishments every quarter. Molloy, Smith, Trezzi, and Wozniak use the other primary administrative data-based source for job creation and job destruction from the Business Dynamics Statistics (BDS), which provides annual job creation and destruction series from the late 1970s to the present. The BDS covers the U.S. private, nonfarm sector.

This brief review of the alternative sources is relevant here because, as noted above, this enables comparisons of the CPS gross flows-based measures with these alternative sources. Molloy, Smith, Trezzi, and Wozniak conduct some of these comparisons in their paper's online appendix, but it is instructive to make detailed comparisons to draw out the relevant conceptual and measurement issues.

In the main body of their paper, Molloy, Smith, Trezzi, and Wozniak do not, for the most part, formally exploit the identities in their equations 1 and 2. I think this is unfortunate for a number of reasons. First, as discussed in detail below, this makes it difficult to compare the findings in this paper with alternative sources and with the findings using these sources in the recent literature. Second, their approach, which uses principal components analysis, involves standardizing the various components of the flows (which they measure as a mix of hazards and flows as a percent of employment), so there are no longer any natural units. But the identities in the authors' equations 1 and 2 make transparent the fact that there are natural units in this setting--the flow of the number of workers. It is common in the literature to use the natural units of the flow of the number of workers and to express each of the components as a percent of total employment. This has the advantage of yielding indicators of labor market fluidity with exact decompositions into the components from the identities in the authors' equations 1 and 2, as well as accompanying related decompositions. Moreover, the components of the decomposition are weighted appropriately. In terms of indicators of fluidity, if there are a greater number of workers flowing from one labor market state to another, we want to take this into account for measuring fluidity. My comments focus on what we can learn from these exact decompositions.

As a starting point, my figure 1 shows the hires from nonemployment (the sum of NE and UE) and separations to nonemployment (the sum of EN and EU) from the CPS gross flows as a percent of employment from 1967:Q3 to 2016: Q (1) These series are constructed directly from the same CPS gross flows series used by Molloy, Smith, Trezzi, and Wozniak. Interestingly, and consistent with one of the authors' main findings, these components of total hires and total separations exhibit a pronounced decline from the late 1960s to the present. However, observe that the declining trend in these indicators is entirely from the 1960s to the early 1990s. Fitting a simple linear trend for the period from 1990 to 2016 yields no evidence of a statistically significant trend. Starting in 1990, there is a modest decline in these indicators through the mid-1990s, but then there is a modest offsetting positive trend from 1996 to the present. For those who are active participants in this literature, this is a puzzle, because alternative survey and administrative sources show signs of a pronounced downward trend in measures of fluidity starting in 1990 that, if anything, accelerates in the post-2000 period.

[FIGURE 1 OMITTED]

In terms of their principal component analysis, Molloy, Smith, Trezzi, and Wozniak supplement these measures of hires and separations with job flows from the BDS and a proxy for job-to-job flows from the CPS. (2) They combine all these measures together with their standardization and principal component analysis. Given that these supplemental measures exhibit downward trends in the post-1990 period and that the standardization inherent in the process weights all components equally, the principal component analysis yields a downward trend in the first principal component in the post-1990 period. For the reasons discussed above, it is difficult to interpret these patterns, given the standardization of the flows. Moreover, the components they add to their principal component analysis are subcomponents of the hires and separations measures they are using. This implies that there is some double counting that also makes it difficult to interpret the principal component analysis.

Focusing on measures that permit exact decompositions in natural units, my figure 2 shows the pace of worker reallocation (hires plus separations) that emerges from the CPS and the measure that emerges from the integrated survey and administrative data (JOLTS+BED). The CPS-based measure in my figure 2 makes use of the identities in the authors' equations 1 and 2, along with both the CPS gross flows-based hires and separations from and to nonemployment, and also the CPS-based job-to-job flow series starting in 1996.

Conceptually, total worker reallocation reflects the total flow of workers changing labor market states between employment and nonemployment plus the flow of workers who change employers. Total worker reallocation has the desirable feature that it can be exactly decomposed into the terms in the authors' equations 1 and 2. It is a measure that has been used frequently in the literature as a summary measure of labor market fluidity (Davis and Haltiwanger 2014). In my figure 2, these measures are reported as a percent of employment.

My figure 2 suggests, at least at first glance, that the CPS and JOLTS+BED worker reallocation measures are closely related. Although there is a level difference, the correlation is quite high (.94). Moreover, both series show evident declines in the pace of worker reallocation in the post-2000 period. How do we reconcile my figures 1 and 2? The component of total worker reallocation in the CPS-based measure in my figure 2 that exhibits a post-1996 decline is job-to-job flows. Without job-to-job flows, my figure 1 shows that the components of worker reallocation involving flows to and from nonemployment based on the CPS gross flows data exhibit no downward trend after 1990.

[FIGURE 2 OMITTED]

Is there evidence that hires and separations from and to nonemployment exhibit a downward trend since 1990 from other data sources? The answer is overwhelmingly yes. One source is the JOLTS+BED data. My figures 3 and 4 present evidence about the alternative components of separations available for the JOLTS+BED data. Total separations are decomposed in the JOLTS into quits, layoffs, and other separations. Other separations are relatively small in magnitude, so I focus on quits and layoffs. My figure 3 shows the pace of quits from the JOLTS+BED data, along with the CPS-based job-to-job flows. The series are very highly correlated (.95) and have similar rates (as a percent of employment). Henry Hyatt, Erika McEntarfer, and I (2015) also show the CPS-based job-to-job flows are very highly correlated with the new LEHD-based job-to-job flows (correlation of .96). Three inferences emerge. First, the CPS-based job-to-job flows have properties that match alternative administrative data of the same concept. Second, the quits measure from the JOLTS+BED data is a first approximation of a proxy for job-to-job flows. This implies that the layoffs measure from the JOLTS+BED data is a proxy for separations to nonemployment. Third, all the alternative sources of job-to-job flows show a pronounced downward trend during the post-2000 period.

[FIGURE 3 OMITTED]

My figure 4 shows the CPS gross flows-based measure of separations to nonemployment, the layoffs series from the JOLTS+BED data, and the job destruction series from the BED for the 1990-2015 period. Davis, Faberman, and I (2012) highlight the very tight link between layoffs and job destruction at the quarterly frequency. For current purposes, the primary issue is the relationship between the trends in the three measures in my figure 4. Fitting a simple linear trend from 1990 to 2015 (using quarterly data), there is a substantial and statistically significant negative trend in layoffs and job destruction during this period. In contrast, there is no statistically significant trend in the CPS gross flows-based separations to nonemployment over this period. The simple linear trends used here are limited relative to the time series methods used by Molloy, Smith, Trezzi, and Wozniak. But the main point made here is that the CPS gross flows-based measures have different properties than other sources.

[FIGURE 4 OMITTED]

The new LEHD job-to-job flows series can be combined with the QWI to generate administrative-based hires from nonemployment and separations to nonemployment, which I refer to as LEHD+QWI. Hyatt, McEntarfer, and I (2015) show these series from 1998:Q2 to 201EQ4 in that paper's figure 3. Consistent with my figure 4, the LEHD+QWI-based series show a pronounced downward trend in separations to nonemployment during this period. In addition, the hires from nonemployment from the LEHD+QWI data exhibit a similar downward trend. Fitting a simple linear trend yields a statistically significant negative trend from 1998:Q2 to 2011 :Q4 for both series, while the CPS-based series from my figure 1 yields a positive and statistically significant trend during this period.

Taking stock, multiple data sources other than the CPS all show a pronounced and accelerating downward trend in measures of hires from nonemployment and separations to nonemployment during the post-1990 period. The CPS gross flows-based series show no such downward trends. The CPS gross flows are the outlier here. The CPS gross flows are arguably based on data and a methodology that is subject to much more sampling and nonsampling error than the series from other sources. As such, this raises questions about inferences from the CPS gross flows vis-a-vis trends during the post-1990 period. Many of the empirical exercises conducted by Molloy, Smith, Trezzi, and Wozniak rely on samples starting in 1980 or at some point in the 1990s for analyses of the possible sources of the changes in the decline in fluidity. Given that this is the period when the CPS gross flows appear to be anomalous, this raises questions about how to interpret these results. For the measurement community, attention needs to be given to why the CPS gross flows yield such different patterns from the alternative sources of hires from and separations to nonemployment.

Beyond these measurement concerns, a limitation of the principal component analysis, with its standardization of series and focus on the first principal component, is that it misses patterns in the different components of the flows that have the potential to shed light on the underlying causes of the decline in fluidity. My figure 5 depicts an exact decomposition of total worker reallocation into job reallocation and churning that has been actively used in the literature (Davis, Haltiwanger, and Schuh 1996; Burgess, Lane, and Stevens 2000; Hyatt and Spletzer 2013; Davis and Haltiwanger 2014). Conceptually, these different components of worker reallocation depicted in my figure 5 are potentially driven by quite different forces.

Job reallocation reflects the expansion, contraction, opening, and closing down of establishments. In this respect, variation in job reallocation is inherently linked to models of firm dynamics. Such models characterize firm dynamics resulting from the interaction of the evolution of the distribution of idiosyncratic, firm-level shocks and the responses of firms to such shocks. The latter reflect potential frictions and distortions not only in labor markets but also in capital adjustment, product markets, and credit markets. The impact of globalization and information technology on how firms are organized internally potentially plays a role in these firm dynamics and is the subject of active research in the firm dynamics literature (Decker and others 2016). This perspective suggests many possible factors underlying the declining pace of labor market fluidity that do not originate with the labor market. In Molloy, Smith, Trezzi, and Wozniak's paper, most of the explanations focus on changes in the structure of labor markets and not on possible changes in firm dynamics due to factors outside the labor market. Referring back to my figure 5, it is interesting that the job reallocation component of total worker reallocation exhibits a pronounced secular decline over the entire post-1990 period.

[FIGURE 5 OMITTED]

The other component of worker reallocation depicted in my figure 5 is excess worker reallocation, or churning. This reflects the flows of workers across jobs and nonemployment in excess of that needed to accommodate the expansion and contraction of businesses. My figure 5 makes clear that churning is at least as important as job reallocation and exhibits quite different fluctuations over time relative to job reallocation. Churning exhibits cyclical fluctuations throughout the 1990s, but it then declines sharply after the 2001 recession and does not recover. It declines further during the Great Recession and again does not recover. The pattern of decline for churning is quite different from job reallocation.

Changing frictions in the labor market (for example, regulations and flexibility of wages) are likely to influence both the job reallocation and churning components of worker reallocation. However, as discussed above, job reallocation is likely to reflect many factors above and beyond frictions in the labor market. Though my figure 5 yields no immediate inferences about these possible alternative factors, the different patterns across these different components suggest that this decomposition is likely to be useful for future research.

Even with the measurement and conceptual issues raised above, I think this paper by Molloy, Smith, Trezzi, and Wozniak makes a valuable contribution to the literature with the articulation, investigation, and summary of what we know so far about a number of interesting hypotheses. From their approach and perspective, they confirm what others have found in terms of the declining labor market fluidity not being driven simply by a changing composition of firms and workers in observable characteristics. In addition, they usefully investigate a number of possible benign and less benign factors that may underlie the decline in labor market fluidity. For example, they explore, in a similar manner to Hyatt and Spletzer (2013), the hypothesis that the decline in fluidity might reflect improved matching in the labor market. This would be a benign factor that might reflect, for example, improved information in the labor market, given the information technology revolution. They find little evidence to support this hypothesis. Although I am sympathetic to this conclusion, this inference is mostly based on exploiting aggregate time series variation in the starting wages for new workers. Many omitted factors may be changing at the aggregate level, such as productivity and the relationship between productivity and wages (an interesting topic in and of itself). Molloy, Smith, Trezzi, and Wozniak recognize these limitations, but this situation highlights the identification challenges that are present in this literature.

To conclude, the final section of Molloy, Smith, Trezzi, and Wozniak's paper has a very useful discussion and summary of directions for future research. Pursuing these topics should have a high priority. As the authors and the recent literature highlight, benign factors may underlie at least some components (or sectors) of the decline in fluidity. However, there is accumulating evidence in this study and the recent literature that there are likely adverse implications for workers. Moreover, there is also much need for further investigation into the productivity effects of reduced fluidity. An open and interesting question is whether the anemic performance of U.S. productivity growth in the post-2000 period is linked to the decline in fluidity.

REFERENCES FOR THE HALTIWANGER COMMENT

Burgess, Simon, Julia Lane, and David Stevens. 2000. "Job Flows, Worker Flows, and Churning." Journal of Labor Economics 18, no. 3: 473-502.

Davis, Steven J., R. Jason Faberman, and John Haltiwanger. 2012. "Labor Market Flows in the Cross Section and over Time." Journal of Monetary Economics 59, no. 1: 1-18.

Davis, Steven J., and John Haltiwanger. 2014. "Labor Market Fluidity and Economic Performance." In Economic Policy Symposium Proceedings: Re-Evaluating Labor Market Dynamics. Jackson Hole, Wyo.: Federal Reserve Bank of Kansas City.

Davis, Steven J., John C. Haltiwanger, and Scott Schuh. 1996. Job Creation and Destruction. MIT Press.

Decker, Ryan, John Haltiwanger, Ron Jarmin, and Javier Miranda. 2016. "Changing Business Dynamism: Volatility of Shocks vs. Responsiveness to Shocks?" Working paper, http://www.rdecker.net/research

Fallick, Bruce, and Charles A. Fleischman. 2004. "Employer-to-Employer Flows in the U.S. Labor Market: The Complete Picture of Gross Worker Flows." Finance and Economics Discussion Series no. 2004-34. Washington: Board of Governors of the Federal Reserve System.

Haltiwanger, John, Henry Hyatt, and Erika McEntarfer. 2015. "Cyclical Reallocation of Workers across Employers by Firm Size and Firm Wage." Working Paper no. 21235. Cambridge, Mass.: National Bureau of Economic Research.

Hyatt, Henry R., and James R. Spletzer. 2013. "The Recent Decline in Employment Dynamics." IZA Journal of Labor Economics 2: article 5.

(1.) Constructing the series in my figure 1 from 1990 forward is straightforward, because the BLS has produced internally consistent stocks and flows since 1990. Before 1990, converting the CPS hazard rates to the percent of employment requires combining the CPS gross flows with the CPS stocks. Though this is more problematic, it is not critical for the issues raised in this paper, given that much of the focus of my comments is on the post-1990 period.

(2.) Instead of using the direct measure based on Fallick and Fleischman (2004), Molloy, Smith, Trezzi, and Wozniak use an alternative measure based on a question in the annual March supplement that asks about the number of employers in the prior year. It turns out that it is highly correlated with the direct measure. I use the direct measure in my comments, because it enables exact decompositions of the authors' equations 1 and 2.

GENERAL DISCUSSION Robert Hall spoke first about what he believed to be one of the most important components of labor market fluidity: the prevalence of extremely short-term jobs, which is a topic studied extensively by Henry Hyatt and James Spletzer. In his own recent work, Hall noted that by observing the distribution of the number of W-2s filed by year across workers, one can observe a decline in the number of workers who file many W-2s in a single year--that is, in the number who hold multiple short-term jobs in a single year. This trend in very short-term job losses is likely an important component of the decline in labor market fluidity. And Hall commended the present paper for not focusing exclusively on month-to-month changes, as many authors have.

The main story of the paper, Hall asserted, was the big decline in matching efficiency in unemployment. A natural question to ask is: If it has become harder and harder to match workers, why is unemployment not rising? The answer, according to Hall, is that entry rates to employment are declining along very much the same trend, and the result is that they exactly offset each other. Unemployment today is exactly the same as it was in 1948, despite matching efficiency being much lower, something he called "an amazing fact."

Hall agreed with discussant John Haltiwanger that the role of large employers in reducing turnover, rationalizing the labor market, and incidentally reducing fluidity is a good thing. Lower labor market fluidity implies lower job turnover, and large employers are able to achieve lower turnover because they have many tools and are efficient at managing it. "Fluidity is a bad thing," he said, "so it is a good thing to reduce it." He concluded that declining job turnover--now interestingly labeled declining labor market fluidity--is very much a fact. The Bureau of Labor Statistics, for instance, has published turnover rates from the Current Population Survey (CPS) all the way back to 1948, and tabulations support the notion that labor market fluidity has been declining for a long time.

Richard Cooper proposed that two changes in the character of the labor force might explain the trends in declining labor market fluidity, and wondered if they might be quantitatively important. The first related to service in the armed forces, which he noted has been a big source of job mobility in the United States over the years. He listed a number of factors related to service in the armed forces that might be relevant, including service members' frequent changing of geographic location and their training in certain skills, as well as the fact that the size of the armed forces has fluctuated considerably over time, particularly after the end of the draft in the 1970s. Second, Cooper wondered how the issue of immigration was treated. He noted that there have been big changes in both the number of immigrants and the treatment of immigrants, in particular the legalizing of a large number of formerly undocumented immigrants. Might these features have any influence on the authors' findings?

Robert Moffitt encouraged the authors to look around in the literature to see what it says on turnover at longer durations than a quarterly or annual period. As an algebraic matter, one could have declining transition rates in short durations and increasing exit rates at somewhat longer durations, and that would be something interesting to know. He suggested that the decline in job-to-job flows might be explained using a Jovanovic-style learning model, in which both parties form a match, but it takes a while for them to each learn whether it is a good match or not. (1) In addition, he noted that there appears to be some very casual evidence that the amount of uncertainty employers have about whether a match is good or not does not rely as heavily as it once did on the usual indicators, such as education, age, and past employment history. Thus, it might take a while longer for employers to learn whether a match is good or not.

However, Moffitt argued that the job-to-job flow component is more important than the not-in-the-labor-force component. He recalled a paper presented at the Fall 1991 Brookings Papers meeting in which Kevin Murphy and Robert Topel documented an increase in the length of time that less educated men were spending completely out of the labor force and simply not working for an extended period. (2) Consequently, this leads to fewer entrants into unemployment. He noted that there was an observable decline in labor force participation starting in about 2000, during which both men and women began spending more and lengthier times completely out of the labor force. Perhaps this fact could shed a somewhat different light on how to interpret turnover in the labor market.

Matthew Shapiro wondered if the authors' analysis could shed light on whether the trend of increasing occupational regulation and licensing is an important factor when it comes to job-to-job flows. He noted that it might be interesting to know whether the decline in labor market fluidity is within industry or between industry, or similarly with occupation. He believed the question could be addressed using the authors' framework, and that it might point to one of their hypotheses.

David Romer had two brief comments. First, he suggested that it might be useful for the authors to calibrate their analysis against other countries, such as those in Europe. He recalled from a Fall 2011 paper by Michael Elsby, Bart Hobijn, Aysegul Sahin, and Robert Valletta that at the height of the Great Recession, the job-finding rate for long-term unemployed workers in the United States was higher than for unemployed workers in France under normal times. (3) This comparison suggests that despite the recent declines in fluidity, the U.S. labor market remains extremely dynamic relative to those of other countries and is very far from exhibiting sclerosis. Second, Romer wondered about the issue of the job market becoming more formal or litigious. He thought that the small amount of evidence the paper provides on this issue is interesting but far from definitive, and that casual empiricism suggests that increased litigiousness might be a nontrivial factor in declining fluidity. As an example, he recalled a conversation he had with a lawyer who represents firms in employment litigation; the lawyer reported that, in California, if an employer fires someone without good cause, the employer would almost certainly be sued.

Valerie Ramey wondered if one possible explanation for the longer-term decline in labor market fluidity was the rise of two-career couples, to the extent that there is now a joint location problem for many people. She suggested that this might explain the decrease in geographic mobility that one sees in the data, along with the decreasing fluidity. In her own work, Ramey had noticed a decline in the marital wage premium for men, which would be consistent with people being stuck in their same jobs because of the joint location problem.

Karen Dynan wondered if the authors could say more about the housing market and its relationship to labor market fluidity. She noted that at least one of the authors (Raven Molloy) is an expert on the housing market, so she suspected that they had probably thought a lot about it. Dynan recalled that in the paper, the authors mentioned having looked at land use regulations, but that they did not find much evidence of a connection to declining labor market fluidity. She noted that land use regulation is an incomplete measure of the elasticity of housing supply, and wondered if the authors had looked at more direct measures. She suggested that the level of home prices might be a more direct indicator of the frictions associated with moving; the higher the level, the more costly it would be to move. Additionally, higher home prices might mean that homeowners have more wealth tied up in housing, which would explain the relationship to the propensity to start new businesses.

Gabriel Chodorow-Reich mentioned some joint work with Johannes Wieland in which he and Wieland play with a model that has both worker shocks and job shocks. (4) In a frictional labor market, it is easier to accommodate job shocks that shift the distribution of labor demand across firms or across industries without increasing aggregate unemployment if gross worker flows are higher. This interaction between changes in the distribution of labor demand and worker fluidity, Chodorow-Reich concluded, suggests that more weight might need to be put on the less benign interpretation of the decline in worker flows.

Discussant Erica Groshen had suggested that the authors consider shorter-duration series, since much of the "light" on the causality from job flows to workers flows will probably be found there. Abigail Wozniak responded with a reminder of what the authors believed they were accomplishing in the paper: They felt it was important to go back as far as possible in order to get a handle on the underlying, long-term factors of labor market fluidity. She agreed with Groshen that much of the "light" is in fact with the shorter-duration series, but that the present analysis seeks to accomplish something different. In response to a question raised by moderator James Stock about the seemingly small role of demographics in their analysis, Wozniak explained that one of the helpful pieces of their approach was the ruling out of the demographics story a bit, so that when they did turn to shorter-duration series--which do not have detailed demographics attached to them--the authors could go forward with confidence.

In response to a point made by Haltiwanger about the CPS and what it shows, Wozniak noted that this was not the first time the authors had run into questions about how the CPS measures job transition. She noted that when it comes to migration declines, the CPS actually shows more pronounced declines than other series, like the American Community Survey, during the same period. Wozniak added that there are probably really important questions about how the CPS picks up job transitions, and whether that technique is changing over time. Nonetheless, she concluded that it would probably not change much about the paper's overall story.

Adding on to comments made about the length of the series, Raven Molloy noted that the authors began by trying to think about whether there was a single explanation for the entire 30- to 35-year period; when a decline is observed over such a long period, it is natural to start by asking if there is one thing that can explain the entire decline. She agreed that there are probably many things going on, and different explanations could be more important for some periods than others, but given that the decline looks pretty steady for the entire period, perhaps there was an explanation that made sense for the entire period. Similarly, the authors thought it was natural to try to find an explanation that could make sense for an entire set of different types of workers and industries. Although it is true that there is interesting variation across different types of workers and industries, it is still the case that declines are observed for many different types of workers and industries, which again suggests that some broad-based explanation might be affecting all workers and industries.

On the question of housing, brought up by Dynan, Molloy stated that it was something the authors had thought a lot about, and she would have loved to have found an explanation for the declines in labor market fluidity related to the housing market. In the end, Molloy could not convince herself that there was a housing story, partly because many declines in job transitions are observed within geographic labor markets. The authors tried to look at job-to-job flows for people who stay within the same state or stay within the same metropolitan area, and they observed that there are also big declines in labor market fluidity in those flows. It is hard to reconcile why there is such a big decline observed in migration rates for these types of people, and even to a greater extent in other kinds of job transitions as well. And in response to a question posed by Ramey, Molloy added that the same thing is true of two-career couples. The authors spent a lot of time in another paper they wrote trying to see if these kinds of explanations could apply to the long-run decline in migration, and again could not really find much evidence. This is partly because the increase in two-career couples was just much too small by the measures that the authors could find in the CPS to really explain much of the decline in migration, and partly because, again, it was a more broad-based phenomenon; there are lots of declines in migration and labor market flows for people who are not married, for example.

Christopher Smith responded to some of the questions related to the data. During her remarks, Groshen had suggested that perhaps when the authors were thinking about explanations, they should think about what explanations have implications for which measures of fluidity, and that there were some other questions about how declines in labor force participation might tie into some of these flows. Smith noted that the authors actually mention this in the paper, and it turns out that when one looks at some of the labor market flows individually, one sees declines that seem as if they are related to the secular declines in participation for particular demographic groups. In particular, the job-finding rates for younger workers and for prime-aged men have both been declining since the mid-1980s, which lines up with their secular declines in participation. On the other hand, the job separation rate for prime-aged women has been falling over this period, which also reflects the rise in participation. When looking at any one measure in isolation, Smith noted, there are going to be idiosyncratic factors or things related to, say, secular declines in participation that might affect that particular measure. But the goal of the paper, he insisted, was to look at a variety of measures in hopes of finding common factors that were running under all of them.

On the question of what the CPS shows about job-to-job transitions since its major redesign in 1994, Smith noted that one can in fact construct a measure using the CPS, referring to it as the Fallick-Fleischman measure. (5) One question in the CPS asks whether you were working for the same employer in the previous month or not, which has been the industry-standard measure for estimating job-to-job transitions from one month to the next. Smith believed that one of the innovations of the present paper was that the authors in a way extend something that looks like that series backward by considering a question from the March CPS that asks respondents about the number of employers where they have worked in the last year, which could also be considered a measure of job-to-job transitions; this trend, he noted, looks virtually identical to the more traditional measures. Extending this series back, one interesting thing that pops out is that from this longer measure of job-to-job transitions, it is pretty much flat until the mid-1990s, which is when the decline begins to be observed. The traditional measure, on the other hand, would not necessarily pick up this clear flatness followed by the decline.

Riccardo Trezzi added a few final points about the authors' time series analysis. Regarding concerns raised about the authors' specific data set and trending methods, Trezzi noted that the paper does contain some robustness checks, in the sense that the biweight filter is not the only filter used in the analysis; the authors also check their results using the Christiano-Fitzgerald band pass filter (6) and the Muller-Watson cosine projection method. (7) Finally, Trezzi added that the reason the authors chose the particular analysis period was that they wanted to get at underlying trends, and did not want to pick up business cycle movements. In order to do this, one has to work on the low-frequency side of the spectrum--and significantly so otherwise the analysis will tend to pick up some business cycle movements, especially because the Great Recession is at the end of the sample, which generates additional issues.

(1.) See for example, Boyan Jovanovic and Yaw Nyarko, "A Bayesian Learning Model Fitted to a Variety of Empirical Learning Curves," Brookings Papers on Economic Activity: Microeconomics, 1995: 247-99.

(2.) Chinhui Juhn, Kevin M. Murphy, and Robert H. Topel, "Why Has the Natural Rate of Unemployment Increased over Time?" Brookings Papers on Economic Activity, no. 2 (1991): 75-126.

(3.) Michael W. L. Elsby, Bart Hobijn, Aysegul Sahin, and Robert G. Valletta, "The Labor Market in the Great Recession--An Update to September 2011," Brookings Papers on Economic Activity, Fall 2011: 353-71.

(4.) Gabriel Chodorow-Reich and Johannes Wieland, "Secular Labor Reallocation and Business Cycles," Working Paper no. 21864 (Cambridge, Mass.: National Bureau of Economic Research, 2016).

(5.) See for example, Bruce Fallick and Charles A. Fleischman, "Employer-to-Employer Flows in the U.S. Labor Market: The Complete Picture of Gross Worker Flows," Finance and Economics Discussion Series no. 2004-34 (Washington: Board of Governors of the Federal Reserve System, 2004).

(6.) Lawrence J. Christiano and Terry J. Fitzgerald, "The Band Pass Filter," International Economic Review 44, no. 2 (2003): 435-65.

(7.) Ulrich K. Muller and Mark W. Watson, "Low-Frequency Econometrics," Working Paper no. 21564 (Cambridge, Mass.: National Bureau of Economic Research, 2015).

RAVEN MOLLOY

Federal Reserve Board

CHRISTOPHER L. SMITH

Federal Reserve Board

RICCARDO TREZZI

Federal Reserve Board

ABIGAIL WOZNIAK

University of Notre Dame

(1.) Davis and others (2010) demonstrate a link between declining job reallocation rates at the state level and worker flows into and out of unemployment. Davis, Faberman, and Haltiwanger (2012) show that hires and separations are linked to job creation and destruction at the establishment level.

(2.) Our calculations are based on data from the Current Population Survey's Annual Social and Economic Supplement from 1999 to 2015.

(3.) For the quarterly series we use a 90-quarter window. This corresponds to about 46 quarters (90/1.93) for an equal-weighted moving average, where the value 1.93 = [(1 - [4/[square root of 30]).sup.-1/2] comes from finding the width of the kernel when its unnormalized value is 1/2.

(4.) This approach has the advantage that it makes no assumption about reversion of the local mean. By contrast, the standard approach imposes mean reversion by using a stationary time series model to pad the series with forecasts and backcasts.

(5.) Data since 2012 are available from the Bureau of Labor Statistics' gross flows statistics. Data through 2012 are from Elsby, Michaels, and Ratner (2015). Their data are derived from three sources. The data for June 1967 to December 1975 were tabulated by Joe Ritter and are available from Hoyt Bleakley. The data for January 1976 through January 1990 were constructed by Robert Shimer (2012), and are available on his website (https://sites.google. com/site/robertshimer/research/flows). The data for February 1990 until 2015 are available from the Bureau of Labor Statistics' gross flows statistics. Later in our analysis, we estimate gross flows by demographic characteristics, which we calculate using monthly CPS data matched with the codes that Robert Shimer provides on his website.

(6.) The Job Openings and Labor Turnover Survey (JOLTS) from the Bureau of Labor Statistics provides alternative measures of worker-level transitions. However, JOLTS data only begin in 2001, and therefore do not provide a long enough period for our primary analysis. Appendix figure A.l compares our CPS-based measures of labor market transitions to JOLTS-based measures, although the measures from these two sources are not entirely comparable because JOLTS-based measures include job-to-job transitions, whereas the CPS-based measures do not. Even so, the trend in the job finding rate looks similar in JOLTS and CPS data. The job separation rate looks rather flat in the CPS data since the mid-1990s, while it has fallen in the JOLTS data. Disaggregating further, the JOLTS layoff rate is very similar to the CPS EU flows (both are roughly flat, on net, during the 2000s); it is the JOLTS quit rate that has been falling more than the CPS EN rate. This difference is likely because job-to-job transitions have been declining (see panel D of appendix figure A. 1). The online appendixes for this and all other papers in this volume may be found at the Brookings Papers web page, www.brookings.edu/bpea, under "Past Editions."

(7.) The Unicon Research Corporation has ceased providing CPS data. The data used by the authors for this portion of the analysis are available upon request.

(8.) See panel D of appendix figure A. 1. The correlation of our estimate with each of these other estimates is .97.

(9.) For the years that the IRS and ACS data overlap, the level and changes in aggregate migration are quite similar (Molloy, Smith, and Wozniak 2011).

(10.) For the quarterly series, we use a 90-quarter window; for the annual series, we use a 30-year window.

(11.) A simple average is not a bad approximation since the PCA assigns roughly equal weights to most series. Using the factor loadings from the PCA as weights also yields a weighted average decline of about 13 percent.

(12.) Decker and others (2014b, 2016) emphasize that the decline in job creation and job destruction appears to have accelerated in about 2000 in some industries. Although an analysis of the inflection points in specific industries is undoubtedly valuable in shedding light on the specific factors affecting labor market flows in these industries, we choose to focus on common trends that persist over the entire 30 to 40 years in hopes of shedding light on contributors to the decline in labor market fluidity that are broad-based across industries and pertain to a long period of time.

(13.) Given the number of observations in our sample, the maximum number of smoothing quarters allowed by the biweight filter is 97.

(14.) The parameters of the Christiano and Fitzgerald (2003) filter are set to retrieve cycles longer than 30 years. For the cosine projection method, we use two cosine functions.

(15.) We estimate the regressions with weighted least squares, weighting by the size of each cell.

(16.) As shown in appendix figure A.3, aging can explain about half of the decline in EN. EU, and NE, but none of the variation in UE. Adding in education also helps explain more of the decline in EN, EU, and NE, but also provides no additional contribution to UE.

(17.) Demographic differences in the various component flows of job finding and job separation rates are consistent with explanations related to labor force participation (appendix figure A.4). For younger workers and prime-age males, NE flows have declined notably. For prime-age women, EN flows have fallen somewhat and NE flows had risen through 2006 or so before dropping back during and after the recession. For persons ages 55 and older, EN flows have also fallen, likely reflecting later retirement ages. Also, and less likely to be related to labor participation decisions, EU flows have fallen a bit for most demographic groups.

(18.) Davis and Haltiwanger (2014) use variation in worker and job reallocation across states to examine the effects of reallocation on employment rates.

(19.) All the results reported below are robust to measuring migration using IRS data rather than CPS data; we prefer CPS data for this purpose because IRS data are not available for the District of Columbia, Alaska, and Hawaii.

(20.) Trend breaks appear to be more common for job destruction, job-to-job transitions, and flows from employment to not in the labor force.

(21.) These estimates are not dissimilar to those shown by Davis and Haltiwanger (2014), who calculate trends for all 51 states as the difference in average job reallocation from 1988-90 to 2008-10. The correlation of our estimates with theirs is.64. Differences between the two sets of estimates are due to a number of methodological differences: We include a wider range of measures of labor market fluidity, our sample period is longer, we control for the business cycle, and we estimate trends using all annual data points rather than taking the difference of the end points.

(22.) In order to interpret the first principal component as an average percent change, we calculate the weighted average of the eight trends in the individual fluidity measures and divide by the weighted average of the initial levels of these measures in 1980. In both cases, the weights used are those on the first principal component of the PCA.

(23.) We do not have enough observations to consider all potential state-level characteristics in a single regression. Although we could regress fluidity on each state characteristic individually, doing so would likely lead to a large number of spurious results because many state-level characteristics are mechanically correlated with other characteristics; for example, states with a large fraction of young people also have a small fraction of old people.

(24.) All state characteristics except union membership are from the CPS-ASEC. Union membership was calculated from the monthly CPS and the Directory of National Unions and Employee Associations by Hirsch, Macpherson, and Vroman (2001).

(25.) Occupations are defined using the 1990 categorization from IPUMS (Ruggles and others 2015).

(26.) We obtain similar results when only the trends in the shares of these occupations are included. However, when both the levels and trends are included, the estimated correlations with the trends become small and insignificant, likely because the correlations of the trends with the initial levels are greater than .9 for both variables.

(27.) Decker and others (2014b) find that the trend decline in job reallocation (defined as job creation plus job destruction) is less steep in the manufacturing sector than in some other industries, like retail and services.

(28.) These results are available upon request.

(29.) This sample is similar to the one we constructed for Molloy, Smith, and Wozniak (2014), but we have made it publicly accessible by omitting use of restricted geocoded variables. We have also updated the data construction in a number of other ways. The details of the data assembly are available upon request.

(30.) The estimation controls for year effects within each cohort, but not across cohorts of the NLS, so one may still worry about cyclical differences across the three NLS cohorts. However, the average unemployment rate was 4.3 percent in the first period, 7.0 percent in the second period, and 6.5 percent in the third period. Therefore, it is unlikely that changes in the cyclical position of the economy over these three cohorts are obscuring a secular increase in starting wages.

(31.) Our estimated returns to tenure are smaller than many others in the literature (Topel 1991; Buchinsky and others 2005) because we are controlling for occupation and industry tenure (Parent 2000). Our estimates are similar in magnitude to those reported by Altonji, Smith, and Vidangos (2013), who model wages, employment transitions, and hours jointly for men in the PSID from 1975 to 1996.

(32.) The implications of more frequent wage adjustment may not be viewed by workers as entirely benign if they dislike compensation volatility. The negative effects of greater compensation flexibility seem unlikely to outweigh the benefits of preserving a good match and reducing turnover costs, but a full welfare accounting of this channel is beyond the scope of this paper.

(33.) These results are available upon request.

(34.) Social capital refers to the density of positive interpersonal relationships (connections) between members of a group.

(35.) The complete text of this and other GSS questions may be viewed in the "General Social Surveys, 1972-2014: Cumulative Codebook," made available by the National Opinion Research Center at http://gss.norc.org/documents/codebook/GSS_Codebook.pdf. This survey question appears there on p. 335, coded with the variable called TRUST Note that the wording for many questions varies slightly across the years.

(36.) The trend in fluidity is the same as the one created in section III. The trend in trust is constructed using the same method as the trend in fluidity: the coefficient on a linear time trend in a regression controlling for the state's unemployment rate and its one-year lag.

(37.) These results are available upon request.

(38.) This holds even under the benign scenarios, as better matching or enhanced compensation adjustments make it less likely that workers obtain a credible outside option with which to bargain.

(39.) The results are similar if we use state-level labor market conditions, allowing us to include year fixed effects in the regression. However, we prefer the specification that uses national conditions because wage offers can come from outside of one's state of residence.

(40.) In the PSID, when the 2007-09 recession and postrecession years are excluded, the coefficient on the minimum unemployment rate in the 2000s falls to -0.017 and is insignificantly different from 0. Otherwise, all estimates in table 7 are robust to excluding that recession, as well as to omitting individuals whose current job has lasted less than one year (for whom initial conditions, best conditions, and contemporaneous conditions are all the same).

(41.) If compensation has become more flexible, then wages with the current employer may adjust more frequently than at the business cycle level. This could explain the decreasing importance of the minimum unemployment rate for wages, but it is difficult to reconcile with a greater role for the initial unemployment rate.

(42.) Involuntary unemployment was measured using the variable WHYUNEMP in the CPS monthly data available from IPUMS for 1976 to 2015 (Ruggles and others 2015).

(43.) We thank Brooks Pierce and Jesus Ranon of the Office of Compensation and Working Conditions at the Bureau of Labor Statistics for tabulating these data for us.

(44.) Cannon and others (2001) find that compensation differences across firms but within occupation groups declined from the early 1980s to the late 1990s. However, they only examine average compensation differences, not changes in the compensation received by workers that change employers.

Table 1. Descriptive Statistics for Low-Frequency
Flow Components, Annual Series, 1975-2014 (a)

                                  Minimum        Maximum

Variable   Mean    Std. Dev.   Value   Year   Value   Year

EU         0.014     0.001     0.014   2014   0.015   1978
UE         0.259     0.008     0.242   2014   0.271   1975
EN         0.029     0.001     0.027   2014   0.032   1975
NE         0.047     0.001     0.047   2014   0.047   1995
JtJ        0.139     0.010     0.119   2014   0.154   1975
IM         0.026     0.001     0.024   2014   0.028   1975
JD         0.149     0.004     0.142   2013   0.156   1977
JC         0.169     0.001     0.154   2013   0.183   1977

Sources: See sources for figures 1 and 2.

(a.) Low-frequency flow components are extracted using a biweight
filter with a window of 30 years. All series are recorded at annual
frequency. For JD and JC, the latest observation in the sample is 2013.
See the notes to figures 1 and 2 for the units of each flow component.

Table 2. Correlation between Low-Frequency Flow
Components, Annual Series, 1977-2013 (a)

        EU       UE       EN       NE      JtJ       IM       JD

UE     .95
      [0.00]
EN     .82      .92
      [0.00]   L0.00]
NE     -.07     -.15     -.48
      [0.61]   [0.28]   [0.00]
JtJ    .99      .99      .93      .04
      [0.00]   [0.00]   [0.00]   [0.76]
IM     .97      .98      .97      -.09     .98
      [0.00]   [0.00]   [0.00]   [0.56]   [0.00]
JD     .99      .99      .94      .03      .99      .99
      [0.00]   [0.00]   [0.00]   [0.83]   [0.00]   [0.00]
JC     .98      .99      .96      -.02     .99      .98      .99
      [0.00]   [0.00]   [0.00]   [0.87]   [0.00]   [0.00]   [0.00]

Sources: See sources for figures 1 and 2.

(a.) Low-frequency flow components are extracted using a biweight filter
with a window of 30 years. All series are recorded at annual frequency.
Significance levels are reported in square brackets.

Table 3. Principal Component Analysis (a)

                     Eigenvalues (b)

              (1) (d)   (2) (e)   (3) (f)

Component 1    2.08      3.87      6.88
              [0.52]    [0.77]    [0.86]
Component 2    1.33      1.10      1.10
              [0.33]    [0.22]    [0.13]
Component 3    0.57      0.01      0.01
              [0.14]    [0.00]    [0.00]
Component 4    0.01      0.00      0.00
              [0.00]    [0.00]    [0.00]
Component 5     --       0.00      0.00
                --      [0.00]    [0.00]
Component 6     --        --       0.00
                --        --      [0.00]
Component 7     --        --       0.00
                --        --      [0.00]
Component 8     --        --       0.00
                --        --      [0.00]

                     Eigenvectors (c)

              (1) (d)   (2) (e)   (3) (f)

EU             0.49      0.50      0.38
UE             0.62      0.50      0.38
EN             0.60      0.48      0.37
NE            -0.02     -0.01      0.00
JtJ             --       0.50      0.38
IM              --        --       0.38
JD              --        --       0.38
JC              --        --       0.38

Sources: See sources for figures 1 and 2.

(a.) Low-frequency flow components are extracted using a biweight
filter with a window of 30 years. The PCA is run three times,
corresponding to the three columns on each side of the table.

(b.) The left-hand panel reports the eigenvalues of the PCA, with
the fraction of total variance explained by each component in
square brackets.

(c.) The right-hand panel shows the entries of the eigenvector
associated with the first component of the PCA.

(d.) The PCA is run on EU, UE, EN, and NE, recorded at quarterly
frequency, from 1967 to 2015.

(e.) The PCA is run on EU, UE, EN, and NE, annualized, plus JtJ,
from 1975 to 2014.

(f.) The PCA is run on EU, UE, EN, and NE, annualized, plus JtJ,
IM, JD, and JC, from 1977 to 2013.

Table 4. Correlations between State-Level Trends in Labor Market
Fluidity and Selected State Characteristics'
                                                           All states,
                                                            excluding
Characteristic                                All states     Alaska

Percent administrative support occupations,    0.29 **      0.33 **
  average level, 1977-79 (b)                  (0.07)       (0.06)
Percent operator occupations, average          0.33 **      0.38 **
  level, 1977-79 (b)                          (0.08)       (0.07)
Percent union member, trend, 1980-2013 (c)    -0.18 **     -0.13 *
                                              (0.07)       (0.06)
Percent ages 35-44, trend, 1980-2013 (c)       0.34 **      0.06
                                              (0.08)       (0.09)
Middle Atlantic                                0.57         0.49 *
                                              (0.28)       (0.24)
Mountain                                      -0.92 **     -0.92 **
                                              (0.21)       (0.17)
Pacific                                       -0.99 **     -0.91 **
                                              (0.25)       (0.21)
Constant                                       0.21 *       0.26 **
                                              (0.08)       (0.07)
No. of observations                           51           50
Adjusted [R.sup.2]                             0.80         0.79

Sources: Current Population Survey, Annual Social and Economic
Supplement, as provided by the Bureau of Labor Statistics, Unicon
Research Corporation, and Ruggles and others (2015); Hirsch,
Macpherson, and Vroman (2001).

(a.) Reports the results of regressing the trend in labor market
fluidity in each state on selected state characteristics (for the
coefficients using the full set of characteristics, see appendix
table A.6). The trend in labor market fluidity is the first
component from a PCA of linear trends of the following annual
variables: EU, UE, EN, NE, JtJ, IM, JC, and JD. Standard errors are
in parentheses. Statistical significance is indicated at the **1
percent and *5 percent levels.

(b.) Defined by the variable occ 1990 from IPUMS.

(c.) Estimated from a state-specific regression on a linear trend
and the state unemployment rate (contemporaneous and one-period
lag) from 1980 to 2013.

Table 5. Average Hourly Wage for Jobs Held for
Less Than One Year for Men Ages 22-33 (a)

                             NLSM        NLSY79    NLSY97

Average wage                10.5         11.6      10.6
                            (0.42)       (0.56)    (0.48)
Average wage in low-        11.2         12.2      10.3
  education subsample       (0.50)       (0.62)    (0.53)
Average wage in high-       13.0         15.2      14.6
  education subsample       (0.67)       (0.97)    (0.69)
No. of observations         3,165        5,450     4,756
Observation years       1966-71, 1973,   1979-94   2002-13
                        1975-76, 1978,
                        1980-81

Sources: National Longitudinal Survey of Older and Young Men, young
men's cohort (NLSM); National Longitudinal Survey of Youth 1979
(NLSY79); National Longitudinal Survey of Youth 1997 (NLSY97).

(a.) Average wages are computed as the constant term in a
regression of real wages on controls for age, race, education, and
the national unemployment rate, using the National Longitudinal
Survey sample indicated in the column headings. Samples are
restricted to those with less than one year of tenure at their main
job. Standard errors are in parentheses.

Table 6. Implied Returns to a Third Year
of Employer Tenure for Men Ages 22-33 (a)

                           NLSM          NLSY79     NLSY97

Industry tenure        0.016 **         0.015 **     0.005
                      (0.006)          (0.005)      (0.007)
Occupation tenure      0.015 **         0.016 ***    0.012
                      (0.006)          (0.004)      (0.007)
Employer tenure       -0.012            0.002        0.0005
                      (0.006)          (0.006)      (0.008)
No. of observations   11,466           19,363       15,842
Observation years     1966-71, 1973,   1979-94      2002-13
                      1975-76, 1978,
                      1980-81

Sources: National Longitudinal Survey of Older and Young Men, young
men's cohort (NLSM); National Longitudinal Survey of Youth 1979
(NLSY79); and National Longitudinal Survey of Youth 1997 (NLSY97).

(a.) Cells show implied returns to three years of tenure in the
designated category, holding other characteristics constant.
Returns are calculated from estimates of equation 6, using the
National Longitudinal Survey sample indicated in the column
headings. Standard errors are in parentheses. Statistical
significance is indicated at the *** 0.1 percent, ** 1 percent, and
* 5 percent levels.

Table 7. Nested Tests of Contracting Models (a)

                 PSID (b,c)   PSID (b,c)   CPS (c,d)

Ages               21-64        22-33        21-64
Years            1981-2013    1981-2013    1979-2012

Current unemployment rate (f)
1980s              0.017 **     0.013 *      0.007 **
                  (0.003)      (0.005)      (0.002)
1990s              0.005        0.010        0.026 **
                  (0.003)      (0.007)      (0.009)
2000s             -0.004       -0.013 **     0.008 *
                  (0.002)      (0.004)      (0.003)

Initial unemployment rate (g)
1980s              0.010        0.011        0.000
                  (0.006)      (0.007)      (0.004)
1990s             -0.001       -0.009        0.007
                  (0.004)      (0.008)      (0.006)
2000s             -0.018 **    -0.017       -0.013 **
                  (0.005)      (0.010)      (0.004)

Minimum unemployment rate (h)
1980s             -0.044 **    -0.035 **    -0.007
                  (0.007)      (0.011)      (0.005)
1990s             -0.060 **    -0.045 **    -0.047 **
                  (0.007)      (0.013)      (0.011)
2000s              0.002        0.010       -0.005
                  (0.007)      (0.012)      (0.006)
No. of
  observations    37.769       14,657       73,416

                 CPS (c,d)    NLSY79 (b,e)   NLSY97 (b)

Ages               22-33         22-33         22-33
Years            1979-2012      1979-94       2002-13

Current unemployment rate (f)
1980s              0.022 **      0.003
                  (0.006)       (0.003)
1990s             -0.060 *
                  (0.024)
2000s              0.000                      -0.003
                  (0.007)                     (0.006)

Initial unemployment rate (g)
1980s              0.005        -0.000
                  (0.008)       (0.005)
1990s             -0.003
                  (0.013)
2000s             -0.040 *                    -0.010
                  (0.016)                     (0.018)

Minimum unemployment rate (h)
1980s             -0.035 **     -0.031 **
                  (0.013)       (0.007)
1990s              0.043
                  (0.025)
2000s              0.024                      -0.017
                  (0.019)                     (0.027)
No. of
  observations    16,610        19,628        7,853

(a.) This table is an updated version of table 11 in Molloy, Smith,
and Wozniak (2014). Standard errors are in parentheses, clustered
by individual for the PS ID and NLSY; robust standard errors are in
parentheses for the CPS. Statistical significance is indicated at
the **1 percent and *5 percent levels.

(b.) Regressions include a quadric time trend, individual fixed
effects, employer tenure, employer tenure squared, age, and age
squared.

(c.) Estimates by decade are estimated from a single regression
with decade dummies and interactions of decade dummies with labor
market conditions.

(d.) Regressions include a quadratic time trend, employer tenure,
employer tenure squared, age, age squared, a dummy for having been
married, a dummy for being nonwhite, and dummies for educational
status, industry, and region.

(e.) Although the results are shown in the 1980s row, note that the
results actually span parts of both the 1980s and the 1990s.

(f.) National unemployment rate for all workers ages 16 and up in
the current survey year for the NLSY or the past calendar year for
the PSID and CPS.

(g.) National unemployment rate in the calendar year when the job
began.

(h.) Minimum of national unemployment rates from the year when the
job began to the current survey year for the NLSY or the past
calendar year for the PSID and CPS.

Table 8. Geographic Dimension of Rising Firm-Level Inequality

                                                       Percent change
                                                       in 90-10 ratio
                                                      of establishment
                                                       compensation,
                                                         1982-90 to
Census division                                           2007-15

New England (CT, ME, MA, NH, RI, VT)                         37
Middle Atlantic (NJ, NY, PA)                                 31
East North Central (IL, IN, Ml, OH, WI)                       7
West North Central (IA, KS, MN, MO, NE, ND, SD)               7
South Atlantic (DE, DC, FL, GA, MD, NC, SC, VA, WV)          40
East South Central (AL, KY, MI, TN)                          -1
West South Central (AR, LA, OK, TX)                          31
Mountain (AZ, CO, ID, MT, NV, NM, UT, WY)                    14
Pacific (AK, CA, HI, OR, WA)                                 34

Source: Bureau of Labor Statistics, Office of Compensation and
Working Conditions, unpublished statistics tabulated by Brooks
Pierce and Jesus Ranon.